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https://github.com/carpedm20/visual-analogy-tensorflow

Tensorflow implementation of "Deep Visual Analogy-Making"
https://github.com/carpedm20/visual-analogy-tensorflow

analogy-transformations deep-learning tensorflow

Last synced: about 1 month ago
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Tensorflow implementation of "Deep Visual Analogy-Making"

Host: GitHub
URL: https://github.com/carpedm20/visual-analogy-tensorflow
Owner: carpedm20
License: mit
Created: 2016-02-10T06:37:46.000Z (over 8 years ago)
Default Branch: master
Last Pushed: 2016-08-10T10:35:38.000Z (about 8 years ago)
Last Synced: 2024-07-31T02:16:21.722Z (about 2 months ago)
Topics: analogy-transformations, deep-learning, tensorflow
Language: Python
Homepage:
Size: 4.78 MB
Stars: 288
Watchers: 16
Forks: 44
Open Issues: 2
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        Deep Visual Analogy-Making

==========================

Tensorflow implementation of [Deep Visual Analogy-Making](http://www-personal.umich.edu/~reedscot/nips2015.pdf). The matlab code of the paper can be found [here](http://www-personal.umich.edu/~reedscot/files/nips2015-analogy.tar.gz).

![model](https://github.com/carpedm20/visual-analogy-tensorflow/raw/83893d866557239a890053b55cb7105ebf54045e/assets/model.png)

This implementation contains a deep network trained end-to-end to perform visual analogy making with

1. Fully connected encoder & decoder networks

2. Analogy transformations by vector addition and deep networks (vector multiplication is not implemented)

3. Regularizer for manifold traversal transformations

This implementation conatins:

1. Analogy transformations of `shape` dataset

    - with objective for vector-addition-based analogies (L_add)

    - with objective for multiple fully connected layers (L_deep)

    - with manifold traversal transformations

Prerequisites

-------------

- Python 2.7 or Python 3.3+

- [Tensorflow](https://www.tensorflow.org/)

- [SciPy](http://www.scipy.org/)

Usage

-----

First, you need to download the dataset with:

    $ ./download.sh

To train a model with `shape` dataset:

    $ python main.py --dataset shape --is_train True

To test a model with `shape` dataset:

    $ python main.py --dataset shape 

Results

-------

Result of analogy transformations of `shape` dataset with fully connected layers (L_deep) after 1 day of training.

**From top to bottom** for each : *Reference*, *output*, *query*, *target*, *prediction*, *manifold prediction* after 2 steps, and *manifold prediction* after 3 steps.

- Change on angle

![training in progress](./assets/rotate_160215.png)

- Change on scale

![training in progress](./assets/scale_160215.png)

- Change on x position

![training in progress](./assets/xpos_160215.png)

- Change on y position

![training in progress](./assets/ypos_160215.png)

(in progress)

Training details

----------------

![training in progress](./assets/loss_160215.png)

Reference

---------

- [NIPS 2015 slide](http://www-personal.umich.edu/~reedscot/files/nips2015-analogy-slides.pptx)

Author

------

Taehoon Kim / [@carpedm20](http://carpedm20.github.io/)